Overview

Dataset statistics

Number of variables12
Number of observations834508
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.4 MiB
Average record size in memory96.0 B

Variable types

DateTime1
Numeric11

Warnings

town is highly correlated with street_nameHigh correlation
flat_type is highly correlated with floor_area_sqft and 1 other fieldsHigh correlation
block is highly correlated with street_name and 1 other fieldsHigh correlation
street_name is highly correlated with town and 2 other fieldsHigh correlation
lease_commence_date is highly correlated with block and 2 other fieldsHigh correlation
remaining_lease is highly correlated with lease_commence_dateHigh correlation
floor_area_sqft is highly correlated with flat_type and 1 other fieldsHigh correlation
price_psqft is highly correlated with resale_priceHigh correlation
resale_price is highly correlated with flat_type and 2 other fieldsHigh correlation
town is highly correlated with street_nameHigh correlation
flat_type is highly correlated with floor_area_sqft and 1 other fieldsHigh correlation
street_name is highly correlated with town and 2 other fieldsHigh correlation
lease_commence_date is highly correlated with street_name and 1 other fieldsHigh correlation
remaining_lease is highly correlated with street_name and 1 other fieldsHigh correlation
floor_area_sqft is highly correlated with flat_type and 1 other fieldsHigh correlation
price_psqft is highly correlated with resale_priceHigh correlation
resale_price is highly correlated with flat_type and 2 other fieldsHigh correlation
town is highly correlated with street_nameHigh correlation
flat_type is highly correlated with floor_area_sqft and 1 other fieldsHigh correlation
street_name is highly correlated with town and 1 other fieldsHigh correlation
lease_commence_date is highly correlated with street_nameHigh correlation
floor_area_sqft is highly correlated with flat_typeHigh correlation
price_psqft is highly correlated with resale_priceHigh correlation
resale_price is highly correlated with flat_type and 1 other fieldsHigh correlation
block is highly correlated with street_name and 2 other fieldsHigh correlation
remaining_lease is highly correlated with street_name and 1 other fieldsHigh correlation
street_name is highly correlated with block and 5 other fieldsHigh correlation
town is highly correlated with street_name and 1 other fieldsHigh correlation
flat_type is highly correlated with resale_price and 2 other fieldsHigh correlation
storey_range is highly correlated with price_psqftHigh correlation
resale_price is highly correlated with flat_type and 4 other fieldsHigh correlation
lease_commence_date is highly correlated with block and 7 other fieldsHigh correlation
flat_model is highly correlated with block and 6 other fieldsHigh correlation
price_psqft is highly correlated with storey_range and 3 other fieldsHigh correlation
floor_area_sqft is highly correlated with street_name and 4 other fieldsHigh correlation
town has 47951 (5.7%) zeros Zeros
storey_range has 161366 (19.3%) zeros Zeros
flat_model has 218654 (26.2%) zeros Zeros
lease_commence_date has 19302 (2.3%) zeros Zeros

Reproduction

Analysis started2021-07-16 06:01:56.749140
Analysis finished2021-07-16 06:03:44.488183
Duration1 minute and 47.74 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

Distinct378
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
Minimum1990-01-01 00:00:00
Maximum2021-06-01 00:00:00
2021-07-16T14:03:44.755341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:44.986644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

town
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.53692116
Minimum0
Maximum26
Zeros47951
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:45.174363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q319
95-th percentile25
Maximum26
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.772458369
Coefficient of variation (CV)0.6199654822
Kurtosis-1.200443893
Mean12.53692116
Median Absolute Deviation (MAD)7
Skewness-0.1259931216
Sum10462161
Variance60.4111091
MonotonicityNot monotonic
2021-07-16T14:03:45.386605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1873779
 
8.8%
2163621
 
7.6%
161390
 
7.4%
1260808
 
7.3%
2058882
 
7.1%
047951
 
5.7%
1046038
 
5.5%
340429
 
4.8%
733964
 
4.1%
430752
 
3.7%
Other values (17)316894
38.0%
ValueCountFrequency (%)
047951
5.7%
161390
7.4%
219823
 
2.4%
340429
4.8%
430752
3.7%
52318
 
0.3%
66451
 
0.8%
733964
4.1%
825737
3.1%
925810
3.1%
ValueCountFrequency (%)
2613990
 
1.7%
2530610
3.7%
2424750
 
3.0%
2311223
 
1.3%
2262
 
< 0.1%
2163621
7.6%
2058882
7.1%
1928537
 
3.4%
1873779
8.8%
1721307
 
2.6%

flat_type
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.137370762
Minimum0
Maximum6
Zeros1106
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:45.549131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.124325711
Coefficient of variation (CV)0.5260321374
Kurtosis0.8133138708
Mean2.137370762
Median Absolute Deviation (MAD)1
Skewness1.088219248
Sum1783653
Variance1.264108303
MonotonicityNot monotonic
2021-07-16T14:03:45.672318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2314371
37.7%
1270536
32.4%
3174358
20.9%
563729
 
7.6%
49896
 
1.2%
01106
 
0.1%
6512
 
0.1%
ValueCountFrequency (%)
01106
 
0.1%
1270536
32.4%
2314371
37.7%
3174358
20.9%
49896
 
1.2%
563729
 
7.6%
6512
 
0.1%
ValueCountFrequency (%)
6512
 
0.1%
563729
 
7.6%
49896
 
1.2%
3174358
20.9%
2314371
37.7%
1270536
32.4%
01106
 
0.1%

block
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2529
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.8094626
Minimum0
Maximum2528
Zeros1325
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:46.016379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1166
median330
Q3597
95-th percentile1561
Maximum2528
Range2528
Interquartile range (IQR)431

Descriptive statistics

Standard deviation456.5613758
Coefficient of variation (CV)0.9759558374
Kurtosis3.79556914
Mean467.8094626
Median Absolute Deviation (MAD)197
Skewness1.915130896
Sum390390739
Variance208448.2899
MonotonicityNot monotonic
2021-07-16T14:03:46.201873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1254272
 
0.5%
2093740
 
0.4%
1153169
 
0.4%
173167
 
0.4%
2293087
 
0.4%
2383079
 
0.4%
1733058
 
0.4%
1332994
 
0.4%
2562974
 
0.4%
2662959
 
0.4%
Other values (2519)802009
96.1%
ValueCountFrequency (%)
01325
0.2%
11471
0.2%
22184
0.3%
31830
0.2%
41323
0.2%
51636
0.2%
61512
0.2%
71214
0.1%
81661
0.2%
91295
0.2%
ValueCountFrequency (%)
25281
 
< 0.1%
25271
 
< 0.1%
25263
< 0.1%
25251
 
< 0.1%
25242
 
< 0.1%
25231
 
< 0.1%
25221
 
< 0.1%
25211
 
< 0.1%
25201
 
< 0.1%
25197
< 0.1%

street_name
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct572
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.8856787
Minimum0
Maximum571
Zeros4631
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:46.392879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1110
median213
Q3355
95-th percentile488
Maximum571
Range571
Interquartile range (IQR)245

Descriptive statistics

Standard deviation153.7811131
Coefficient of variation (CV)0.6689460343
Kurtosis-1.040854138
Mean229.8856787
Median Absolute Deviation (MAD)122
Skewness0.2124285996
Sum191841438
Variance23648.63076
MonotonicityNot monotonic
2021-07-16T14:03:46.574051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21816232
 
1.9%
913735
 
1.6%
312836
 
1.5%
111277
 
1.4%
2738698
 
1.0%
1857758
 
0.9%
106960
 
0.8%
136894
 
0.8%
26736
 
0.8%
2126174
 
0.7%
Other values (562)737208
88.3%
ValueCountFrequency (%)
04631
 
0.6%
111277
1.4%
26736
0.8%
312836
1.5%
45960
0.7%
51628
 
0.2%
62337
 
0.3%
7782
 
0.1%
8270
 
< 0.1%
913735
1.6%
ValueCountFrequency (%)
57119
 
< 0.1%
5709
 
< 0.1%
56962
 
< 0.1%
56851
 
< 0.1%
56774
 
< 0.1%
56657
 
< 0.1%
56521
 
< 0.1%
564123
< 0.1%
563291
< 0.1%
56225
 
< 0.1%

storey_range
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016930934
Minimum0
Maximum24
Zeros161366
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:46.726422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.986307884
Coefficient of variation (CV)0.9848170062
Kurtosis15.8558031
Mean2.016930934
Median Absolute Deviation (MAD)1
Skewness2.967187697
Sum1683145
Variance3.945419011
MonotonicityNot monotonic
2021-07-16T14:03:46.892649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1210886
25.3%
2190110
22.8%
3170456
20.4%
0161366
19.3%
453591
 
6.4%
620203
 
2.4%
59726
 
1.2%
86302
 
0.8%
72696
 
0.3%
152689
 
0.3%
Other values (15)6483
 
0.8%
ValueCountFrequency (%)
0161366
19.3%
1210886
25.3%
2190110
22.8%
3170456
20.4%
453591
 
6.4%
59726
 
1.2%
620203
 
2.4%
72696
 
0.3%
86302
 
0.8%
91142
 
0.1%
ValueCountFrequency (%)
2411
 
< 0.1%
2332
 
< 0.1%
2230
 
< 0.1%
212
 
< 0.1%
207
 
< 0.1%
1939
 
< 0.1%
1892
 
< 0.1%
17265
 
< 0.1%
161254
0.2%
152689
0.3%

flat_model
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.471407105
Minimum0
Maximum19
Zeros218654
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:47.041743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.305829455
Coefficient of variation (CV)1.337630473
Kurtosis5.227874159
Mean2.471407105
Median Absolute Deviation (MAD)1
Skewness2.330061455
Sum2062409
Variance10.92850838
MonotonicityNot monotonic
2021-07-16T14:03:47.173203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2232339
27.8%
0218654
26.2%
1176515
21.2%
453893
 
6.5%
339888
 
4.8%
1337151
 
4.5%
632535
 
3.9%
727314
 
3.3%
159131
 
1.1%
162106
 
0.3%
Other values (10)4982
 
0.6%
ValueCountFrequency (%)
0218654
26.2%
1176515
21.2%
2232339
27.8%
339888
 
4.8%
453893
 
6.5%
51919
 
0.2%
632535
 
3.9%
727314
 
3.3%
8657
 
0.1%
943
 
< 0.1%
ValueCountFrequency (%)
1965
 
< 0.1%
18153
 
< 0.1%
17311
 
< 0.1%
162106
 
0.3%
159131
 
1.1%
1483
 
< 0.1%
1337151
4.5%
121125
 
0.1%
11512
 
0.1%
10114
 
< 0.1%

lease_commence_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.00531451
Minimum0
Maximum53
Zeros19302
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:47.340680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median16
Q328
95-th percentile37
Maximum53
Range53
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.49658112
Coefficient of variation (CV)0.7348632752
Kurtosis-0.7959343856
Mean17.00531451
Median Absolute Deviation (MAD)10
Skewness0.4680313186
Sum14191071
Variance156.1645396
MonotonicityNot monotonic
2021-07-16T14:03:47.544232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
682157
 
9.8%
459529
 
7.1%
2047501
 
5.7%
2140170
 
4.8%
238465
 
4.6%
936126
 
4.3%
2230249
 
3.6%
529897
 
3.6%
328743
 
3.4%
3028462
 
3.4%
Other values (44)413209
49.5%
ValueCountFrequency (%)
019302
 
2.3%
119853
 
2.4%
238465
4.6%
328743
 
3.4%
459529
7.1%
529897
 
3.6%
682157
9.8%
719359
 
2.3%
812452
 
1.5%
936126
4.3%
ValueCountFrequency (%)
5310
 
< 0.1%
5214
 
< 0.1%
51759
 
0.1%
503041
0.4%
496174
0.7%
482302
 
0.3%
473794
0.5%
461980
 
0.2%
453558
0.4%
441056
 
0.1%

remaining_lease
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.78900742
Minimum44
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:47.738750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile62
Q174
median82
Q389
95-th percentile94
Maximum96
Range52
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.988283617
Coefficient of variation (CV)0.1236341915
Kurtosis-0.1912553966
Mean80.78900742
Median Absolute Deviation (MAD)7
Skewness-0.6688467654
Sum67419073
Variance99.76580961
MonotonicityNot monotonic
2021-07-16T14:03:47.925359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9446878
 
5.6%
9342917
 
5.1%
8634301
 
4.1%
9233152
 
4.0%
8532868
 
3.9%
8432659
 
3.9%
8832110
 
3.8%
8731725
 
3.8%
8230697
 
3.7%
9030684
 
3.7%
Other values (43)486517
58.3%
ValueCountFrequency (%)
4446
 
< 0.1%
45132
 
< 0.1%
46218
 
< 0.1%
47334
 
< 0.1%
48468
 
0.1%
49603
0.1%
50691
0.1%
51887
0.1%
521078
0.1%
531482
0.2%
ValueCountFrequency (%)
96548
 
0.1%
958753
 
1.0%
9446878
5.6%
9342917
5.1%
9233152
4.0%
9128212
3.4%
9030684
3.7%
8930077
3.6%
8832110
3.8%
8731725
3.8%

floor_area_sqft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct209
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1031.196745
Minimum301.3892
Maximum3304.5173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:48.088507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum301.3892
5-th percentile645.834
Q1785.7647
median1001.0427
Q31227.0846
95-th percentile1560.7655
Maximum3304.5173
Range3003.1281
Interquartile range (IQR)441.3199

Descriptive statistics

Standard deviation279.6785057
Coefficient of variation (CV)0.2712174056
Kurtosis-0.374609383
Mean1031.196745
Median Absolute Deviation (MAD)215.278
Skewness0.370672141
Sum860541933.1
Variance78220.06657
MonotonicityNot monotonic
2021-07-16T14:03:48.260776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
721.181362968
 
7.5%
1119.445644166
 
5.3%
731.945235027
 
4.2%
904.167633809
 
4.1%
1302.431927377
 
3.3%
785.764726287
 
3.1%
990.278824921
 
3.0%
1108.681724777
 
3.0%
979.514924776
 
3.0%
699.653524287
 
2.9%
Other values (199)506113
60.6%
ValueCountFrequency (%)
301.389231
 
< 0.1%
312.1531351
< 0.1%
333.6809724
0.1%
365.972667
 
< 0.1%
376.736521
 
< 0.1%
398.264311
 
< 0.1%
409.0282134
 
< 0.1%
419.7921137
 
< 0.1%
430.556697
0.1%
441.3199351
< 0.1%
ValueCountFrequency (%)
3304.51731
 
< 0.1%
3196.87832
 
< 0.1%
3013.8924
 
< 0.1%
2863.19744
 
< 0.1%
2809.37796
 
< 0.1%
2787.85012
 
< 0.1%
2690.9753
 
< 0.1%
2680.21113
 
< 0.1%
2647.91942
 
< 0.1%
2615.627716
< 0.1%

price_psqft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68088
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.3687721
Minimum14.98437579
Maximum1185.651721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:48.466313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14.98437579
5-th percentile107.4912247
Q1208.6747226
median261.2900529
Q3357.3197304
95-th percentile499.1809965
Maximum1185.651721
Range1170.667345
Interquartile range (IQR)148.6450079

Descriptive statistics

Standard deviation121.6077674
Coefficient of variation (CV)0.4246544289
Kurtosis2.045085612
Mean286.3687721
Median Absolute Deviation (MAD)66.63587592
Skewness0.9573344191
Sum238977031.3
Variance14788.44909
MonotonicityNot monotonic
2021-07-16T14:03:48.635782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
232.25782483075
 
0.4%
185.80625982723
 
0.3%
371.61251961897
 
0.2%
278.70938971780
 
0.2%
464.51564951670
 
0.2%
232.25782481423
 
0.2%
309.67709971418
 
0.2%
214.39183821183
 
0.1%
65.170852321143
 
0.1%
348.38673711140
 
0.1%
Other values (68078)817056
97.9%
ValueCountFrequency (%)
14.984375791
 
< 0.1%
16.782500891
 
< 0.1%
17.08218841
 
< 0.1%
17.381875921
 
< 0.1%
17.981250953
< 0.1%
19.221337221
 
< 0.1%
20.079063561
 
< 0.1%
20.978126116
< 0.1%
21.419332731
 
< 0.1%
21.877188665
< 0.1%
ValueCountFrequency (%)
1185.6517211
< 0.1%
1104.2200581
< 0.1%
1097.0475981
< 0.1%
1095.2790051
< 0.1%
1092.2629671
< 0.1%
1090.8625581
< 0.1%
1079.0311441
< 0.1%
1076.877141
< 0.1%
1075.7204521
< 0.1%
1072.3393191
< 0.1%

resale_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8642
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299175.2295
Minimum5000
Maximum1268000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-16T14:03:48.828371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile87000
Q1185000
median280000
Q3390000
95-th percentile573000
Maximum1268000
Range1263000
Interquartile range (IQR)205000

Descriptive statistics

Standard deviation151875.4302
Coefficient of variation (CV)0.5076470752
Kurtosis1.123293377
Mean299175.2295
Median Absolute Deviation (MAD)100388
Skewness0.8392258603
Sum2.496641224 × 1011
Variance2.30661463 × 1010
MonotonicityNot monotonic
2021-07-16T14:03:49.034469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000006443
 
0.8%
2800006274
 
0.8%
3500006113
 
0.7%
2500006102
 
0.7%
3200006027
 
0.7%
2600005799
 
0.7%
2700005599
 
0.7%
4000005564
 
0.7%
3600005541
 
0.7%
3800005510
 
0.7%
Other values (8632)775536
92.9%
ValueCountFrequency (%)
50001
 
< 0.1%
56001
 
< 0.1%
57001
 
< 0.1%
58001
 
< 0.1%
60004
 
< 0.1%
67001
 
< 0.1%
70008
< 0.1%
730019
< 0.1%
750013
< 0.1%
76001
 
< 0.1%
ValueCountFrequency (%)
12680001
 
< 0.1%
12580001
 
< 0.1%
12500001
 
< 0.1%
12480001
 
< 0.1%
12388001
 
< 0.1%
12320001
 
< 0.1%
12200001
 
< 0.1%
12188881
 
< 0.1%
12100003
< 0.1%
12080002
< 0.1%

Interactions

2021-07-16T14:02:54.610620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:55.135021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:55.548419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:55.886140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:56.268159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:57.506242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:57.888025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:58.242798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:58.599826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:58.927782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:59.277065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:59.632125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:02:59.962094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:00.334247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:00.710710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:01.073592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:01.444768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:01.830956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:02.189271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:02.547283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:02.890596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:03.240597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:03.598034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:03.938589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:04.256119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:04.621912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:04.968853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:05.331310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:05.717725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:06.051937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:06.424526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:06.772250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:07.110520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:07.461750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:07.802165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:08.178830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:08.528164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:08.917962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:09.277437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:09.671634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:10.143087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:10.505823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:10.851209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:11.218516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:11.593197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:11.948168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:12.325828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:12.696518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:13.050388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:13.424873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:13.806355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:14.170857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:14.522008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:14.871204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:15.206467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:15.618075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:16.020158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:16.443795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:16.833070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:17.281367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:17.643105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:18.040454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:18.431250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:18.791221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:19.148561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:19.537179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:19.906785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:20.247490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:20.645269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:21.017854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:21.400934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:21.749268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:22.104228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:22.470461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:22.838635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:23.181573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:23.550115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:23.924739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:24.260747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:24.647057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:24.993582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:25.374489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:25.728537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:26.210020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:26.582074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:26.908716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:27.252825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:27.627568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:27.961619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:28.284971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:28.651709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:29.002712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:29.347492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:29.726345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:30.070841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:30.446493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:30.780241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:31.130174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:31.527705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:31.916369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:32.238907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:32.619369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:32.980895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:33.357004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:33.727250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:34.085007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:34.514278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:34.884523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:35.232781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:35.624803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:35.987031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:36.320858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:36.714862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:37.054433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:37.408830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:37.806381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:38.158281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:38.549706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:38.917231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:39.294079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-16T14:03:39.700960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-16T14:03:49.218965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-16T14:03:49.506931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-16T14:03:49.767292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-16T14:03:50.036561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-07-16T14:03:40.046947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-16T14:03:41.578047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetownflat_typeblockstreet_namestorey_rangeflat_modellease_commence_dateremaining_leasefloor_area_sqftprice_psqftresale_price
01990-01-01000000086333.680926.9718769000.0
11990-01-01000010086333.680917.9812516000.0
21990-01-01000000086333.680923.9750018000.0
31990-01-01000020086333.680917.9812516000.0
41990-01-01011011184785.764760.06887347200.0
51990-01-01012131086721.181363.78423846000.0
61990-01-01013121086721.181358.23778342000.0
71990-01-01014101086721.181352.69132738000.0
81990-01-01014111086721.181355.46455540000.0
91990-01-01015131086721.181365.17085247000.0

Last rows

datetownflat_typeblockstreet_namestorey_rangeflat_modellease_commence_dateremaining_leasefloor_area_sqftprice_psqftresale_price
8344982021-06-012137052182021641302.4319410.770037535000.0
8344992021-06-0121380222006621345.4875401.341521540000.0
8345002021-06-012134442233021641313.1958318.983658418888.0
8345012021-06-0121325165582051941216.3207487.535894593000.0
8345022021-06-0121323915580050931216.3207476.847923580000.0
8345032021-06-0121323755580050931205.5568510.137722615000.0
8345042021-06-012152782583625691948.2659445.524402868000.0
8345052021-06-01215451223376621636.1128357.554809585000.0
8345062021-06-01215682873721641571.5294381.793685600000.0
8345072021-06-012158173031720651571.5294454.970807715000.0